Visualising Networks in ASOIAF - Part II

This is the second post of a character network analysis of George R. R. Martin’s A Song Of Ice and Fire (ASOIAF) series as well as my first submission to the R Bloggers community. A warm welcome to all readers out there! In my first post, I touched on the Tidygraph package to manipulate dataframes and ggraph for network visualisation as well as some tricks to fix the position of nodes when ploting multiple graphs containing the same node set and labeling based on polar coordinates. [Read More]

Visualising Networks in ASOIAF

While waiting for the winds of winter to arrive, there is plenty of time to revisit the 5 books. One of my favourite aspects of the series is the character and world building. As the song of ice and fire universe is so big, many characters are mentioned in passing while the major characters meet each other only occasionally. I thought it would be interesting to see how various characters are connected and how that progresses through the series. [Read More]

Applications of DAGs in Causal Inference

Introduction Two years ago I came across Pearl’s work on using directed cyclical graphs (DAGs) to model the problem of causal inference and have read the debate between academics on Pearl’s framework vs Rubin’s potential outcomes framework. Then I found it quite intriguing from a scientific methods and history perspective how two different formal frameworks could be developed to solve a common goal. I read a few papers on the DAG approach but without fully understanding how it could be useful to my work filed it away in the back of my mind (and computer folder). [Read More]

Choosing a Control Group in a RCT with Multiple Treatment Periods

Came across a fun little problem over the past few weeks that is related to the topic of policy impact evaluation - a long time interest of mine! Here’s the setting: we have a large population of individuals and a number of treatments that we want to gauge the effectiveness of. The treatments are not necessarily the same but are targeted towards certain sub-segments in the population. Examples of such situations include online ad targeting or marketing campaigns. [Read More]

Mapping SG - Shiny App

While my previous posts on the Singapore census data focused mainly on the distribution of religious beliefs, there are many interesting trends that could be observed on other characteristics. I decided to pool the data which I have cleaned and processed into a Shiny app. Took a little longer than I expected but it is done. Have fun with it and hope you learn a little bit more about Singapore! [Read More]

Using Leaflet in R - Tutorial

Here’s a tutorial on using Leaflet in R. While the leaflet package supports many options, the documentation is not the clearest and I had to do a bit of googling to customise the plot to my liking. This walkthrough documents the key features of the package which I find useful in generating choropleth overlays. Compared to the simple tmap approach documented in the previous post, creating a visualisation using leaflet gives more control over the final outcome. [Read More]

Examining the Changes in Religious Beliefs - Part 2

In a previous post, I took a look at the distribution of religious beliefs in Singapore. Having compiled additional characteristics across 3 time periods (2000, 2010, 2015), I decided to write a follow-up post to examine the changes across time. The dataset that I will be using is aggregated from the 2000 and 2010 Census as well as the 2015 General Household Survey. [Read More]

Mapping the Distribution of Religious Beliefs in Singapore

Inspired by my thesis, I have been playing around with mapping tools over the past few days. While the maps showing the distribution of migrant groups across the United States did not make it to the final copy of my paper I had fun toying around with the various mapping packages. In this post, I decided to apply what I have learnt and take a look at the spatial distribution of Singapore’s population. [Read More]

Thesis Thursday 7 - Conclusion

Finally, the last installment of the Thesis Thursday series! Rather than going through what I have done since the previous post (basically more refinements and robustness checks), I decide share some miscellaneous thoughts and lessons learnt over the past few months. The completed research paper and accompanying slides can be downloaded from my website. On R and Stata I decided to code the entire project in R this time round and I have to say that I am quite won over by the capabilities of the various packages. [Read More]